Cada año, un comité de expertos debe acometer una ardua tarea: de entre todas las publicaciones de ICREA, debe escoger unas cuantas que destaquen del resto. Es todo un reto: a veces los debates se acaloran, y siempre son difíciles, pero acaba saliendo una lista con las mejors publicaciones del año. No se concede ningún premio, y el único reconocimiento adicional es el honor de ser resaltado en la web de ICREA. Cada publicación tiene algo especial, ya sea una solución especialmente elegante, un éxito espectacular en los medios de comunicación o la simple fascinación por una idea del todo nueva. Independientemente de la razón, se trata de los mejores de los mejores y, como tales, nos complace compartirlos aquí.


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  • Green gentrification in European and North American Cities (2022)

    Anguelovski, Isabelle (UAB)

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    Green gentrification in European and North American Cities

    Although urban greening is universally recognized as an essential part of sustainable and climate-responsive cities, a growing literature on green gentrification argues that new green infrastructure, and greenspace in particular, can contribute to gentrification, thus creating social and racial inequalities in access to the benefits of greenspace and further environmental and climate injustice. In response to limited quantitative evidence documenting the temporal relationship between new greenspaces and gentrification across entire cities, let alone across various international contexts, we employ a spatially weighted Bayesian model to test the green gentrification hypothesis across 28 cities in 9 countries in North America and Europe. Here we show a strong positive and relevant relationship for at least one decade between greening in the 1990s-2000s and gentrification that occurred between 2000-2016 in 17 of the 28 cities. Our results also determine whether greening plays a “lead”, “integrated”, or “subsidiary” role in explaining gentrification. 

  • Antibiotics as potential anti-metastatic therapies? (2022)

    Aznar Benitah, Salvador (IRB Barcelona)

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    Antibiotics as potential anti-metastatic therapies?

    Metastatic cells require burning long chain fatty acids to promote metastasis (Pascual et al, Nature 2017; Pascual et al., Nature 2021; Martinez et al., Cell Metabolism 2022). However, we still do not know how metastatic cells outcompete their non-metastatic counterparts that are adjacent to them in within their same microenvironment to preferentially obtain energy from fatty acids within the primary tumors. In this paper we found that metastatic cells not only uptake more fatty acids through the fatty acid transporter CD36, buyt also express much higher levels than their non-metastatic counterparts of a mitochondrial specific methionine tRNA methyltransferase which enables them to express certain components of the electron transport chain much more efficiently. This makes them more efficient in burning fatty acids by beta-oxidation then the non-metastatic cells. 

  • A multinational Delphi consensus to end the COVID-19 public health threat (2022)

    Bassat Orellana, Quique (ISGlobal)

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    A multinational Delphi consensus to end the COVID-19 public health threat

    In 2022, Quique Bassat contributed to a breakthrough publication: 'A multinational Delphi consensus to end the COVID-19 public health threat' in the renowned journal Nature. This Delphi study incorporated a diverse, multidisciplinary panel of 386 academic, health, non-governmental organization, government, and other experts in COVID-19 response from 112 countries and territories to recommend specific actions to end this persistent global threat to public health. 


    The panel presented 41 consensus statements and 57 recommendations across six domains–communication; health systems; vaccination; prevention; treatment and care; and inequities–to address the issue of COVID-19 across the globe.
    Three of the highest-ranked recommendations are: i) adopt a whole-of-society strategy that involves multiple disciplines, sectors and actors to avoid fragmented efforts; ii) whole-of-government approaches (e.g. coordination between ministries) to identify, review, and address resilience in health systems and make them more responsive to people’s needs; and iii) maintain a vaccines-plus approach, which includes a combination of COVID-19 vaccination, other structural and behavioural prevention measures, treatment, and financial support measures. The panellists also prioritised recommendations for developing technologies (vaccines, therapies and services) that can reach target populations. Other recommendations with at least 99% agreement were: communicating effectively with the public, rebuilding public trust, and engaging communities in managing the pandemic response.

    The 57 recommendations are directed at governments, health systems, industry, and other key stakeholders. To the greatest degree possible, our results place emphasis on health and social policy recommendations that can be implemented in months, not years, to help bring this public health threat to an end, says Quique Bassat, ICREA professor at ISGlobal, co-author of the study, and member of the University of Barcelona. 
    For more insights about the main findings and suggestions, take a look at the full article here:

  • Shedding light on the treatment of colorectal cancer (2022)

    Batlle Gómez, Eduard (IRB Barcelona)

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    Shedding light on the treatment of colorectal cancer

    Colorectal cancer (CRC) relies on a population of cancer stem cells that express the stem cell marker gene LGR5 on the cell surface. Through functional screenings in patient-derived organoids, we generated MCLA-158, a biospecific antibody that inhibit the growth of LGR5+ cancer stem cells. We showed that MCLA-158 targets cancer stem cells but spare healthy stem cells. This bi-specific antibody showed anti-tumor activity in multiple in vitro and in vivo preclinical models. It is currently being tested in phase 1 clinical trials for different types of solid tumors with very significant responses.

    In CRC patients with overt metastases, chemotherapy initially halts tumor growth, but almost inevitably, the disease progresses after some cycles of treatment. Previous studies have shown that patient-derived organoids predict responses to chemotherapy. Whereas CRC organoids expand from highly proliferative cancer stem cells, we discovered that lack of optimal growth conditions specifies a latent cancer stem cell population. These cells expressed the gene Mex3a. 

    Using lineage-tracing analysis combined with single-cell profiling, we showed that drug-tolerant persister Mex3a+ cells downregulate the cancer stem cell program immediately after chemotherapy and adopt a transient regenerative state reminiscent of that of fetal intestinal progenitors. In contrast, Mex3a-deficient tumor cells differentiate towards a goblet cell-like phenotype and are unable to resist chemotherapy. Overall, our findings reveal how pre-existing cell heterogeneity imposed by adaption to different stem cell niches shape chemotherapy responses in CRCs and may help develop strategies to improve the outcome of current treatments by targeting the Mex3a+ drug-persister cell population.

    In addition, we studied the identity and features of the residual tumor cells responsible for CRC relapse. By analyzing the transcriptomes of individual tumor cells in multiple primary CRC patient samples, we discovered that genes associated with an elevated risk of metastatic relapse are expressed by a defined subset of tumor cells that we named High Relapse Cells (HRCs).

    To investigate HRCs, we established a human-like CRC mouse model that undergoes metastatic relapse after the surgical resection of the primary tumor. We also developed a methodology to isolate residual disseminated tumor cells before metastases are detectable. Using these advances, we demonstrated that residual tumor cells occult in mouse livers after primary CRC surgery resembled the HRC population present in patients. Over time, HRCs gave rise to multiple cell types and generated macrometastases that can kill the host. Genetic ablation of HRCs prior to extirpation of the primary CRC prevented metastatic recurrence and mice remained disease-free after surgery.

  • Optical fingerprinting of superconductivity (2022)

    Biegert, Jens (ICFO)
    Lewenstein, Maciej Andrzej (ICFO)

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    Optical fingerprinting of superconductivity

    Superconductors are materials that exhibit the ability to conduct electricity without any resistance. This phenomenon is observed in materials when they are cooled below the so-called superconductor transition temperature, often at very low temperatures (a few degrees above the absolute 0). Among these materials, there are the so-called high-temperature superconductors, which behave as superconductors at temperatures above 77K (the boiling point of liquid nitrogen). These materials are showing to be essential in the development of new electronic and information processing devices as well as optical quantum computers and even for improving the efficiency of electrical transmission lines.

    However, high-temperature superconductivity has been seen to be closely linked to the control of their microscopic dynamics. So far, the detection of the different microscopic quantum phases in these complex materials has resulted quite challenging. Not only are the physical processes of these dynamic states still incomplete due to their wide array of quantum states, but the current methods used to explore their dynamics at microscopic scales are lagging sensitivity. Therefore, new tools to better understand the dynamic evolution of these types of superconductors are needed.

    Now, an international team of researchers, led by ICREA Professors Jens Biegert and Maciej Lewenstein, propose a new methodology based on the use of High Harmonic spectroscopy (HHS) to investigate the transitions between the different phases of  YBCO, a copper oxide cuprate material which is a well-known high-temperature superconductor. This study represents a major scientific breakthrough since it is the first time that highly non-linear and non-perturbative diagnostics/detection methodology is used to understand the behavior of strongly correlated materials.

    In view of the experimental results obtained, the researchers have also gone beyond and present a new theoretical model to identify the connection between the measured optical spectra and the transition between the different quantum states of the YBCO: strange metal, pseudogap, and superconductor.

  • A new twist on modelling materials interfaces (2022)

    Bromley, Stefan T. (UB)

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    A new twist on modelling materials interfaces

    From Bronze Age alloys to advanced ceramics, technological progress heavily relies on the development and use of polycrystalline materials whose performance is largely dictated by crystallite interfaces (i.e. grain boundaries). Interfaces between different materials are also key to designing heterostructures for use in a range of modern applications (e.g. solar cells, photocatalysts, quantum dot displays). Here, the formation of well-ordered interfaces is achieved by controlled deposition of one semiconductor on the surface of another which is largely constrained by epitaxial matching. Recently, top-down manipulation of two-dimensional (2D) materials has created a new class of layered heterostructures in which epitaxial constraints are less pronounced due to the relatively weak van der Waals interfacial interactions. The resulting freedom to carefully tune the interfaces in such systems (e.g. relative in-plane twist angles of layers) is highly promising for developing the next generation of 2D nanodevices and has already yielded spectacular new phenomena.

    Although clearly playing a huge role in established and emergent technologies, interfaces are highly complex systems whose properties are typically difficult to predict and/or rationalise. Computational modelling is playing an increasingly important role in helping to analyse and understand interfaces. Recent methodological advances have tended to focus on approaches for searching for detailed low-energy atomic/electronic structures of selected interfaces. However, given the huge number of possible ways in which two surfaces can interact, efficient and accurate screening of the energetic/structural landscape of viable interfaces is a pre-requisite for more in-depth investigations. Machine learning has been used to screen the structures and energies of metallic tilt grain boundaries, but required prior training with 10,000’s of calculated examples. Here we address the screening challenge with a simple, powerful and direct modelling approach which, in principle, allows for rapid, unconstrained and systematic exploration of energies and structures of interfaces between arbitrary solid surfaces.